Building a Modern Data Platform for E-Commerce Analytics
A Global e-commerce company wanted to enhance its data analytics capabilities to gain deeper insights into customer behaviour, optimize its operations, and improve its overall business strategies.
The company was facing challenges with their existing Data Infrastructure, which was fragmented, lacked scalability, and couldn’t provide real-time insights. Key decision makers opted for Intellify’s Modern Data Platform Assessment workshop and got a feasible and end to end Modern data platform solution addressing these challenges and drive Data-Driven Decision-Making across the organization.
Key Results
%
Reduction in Service Requests
%
Reduction in Inventory Carrying Cost
%
Increase in Traffic to Conversion Ratio
Goals
- Centralized Data Repository: Create a unified repository for storing all types of data, including customer transactions, website interactions, inventory levels, and marketing campaigns.
- Real-time Analytics: Enable real-time processing of data to provide instant insights for timely decision-making.
- Scalability: Build a platform that could handle growing data volumes and future business expansion.
- Self-Service Analytics: Empower business users to access and analyze data independently without heavy reliance on the IT department.
- Advanced Analytics: Support machine learning and predictive analytics to uncover hidden patterns and trends.
Solution
The e-commerce company collaborated with a Intellify Solutions to Design and Implement a Modern Data Platform. The solution included the following components:
A. Data Ingestion Layer: Utilized tools like Apache Kafka and AWS Kinesis to capture real-time data streams from various sources, including customer interactions, sales, and inventory.
B. Data Storage and Processing Layer:
Data Lake: Built a scalable data lake using cloud-based storage (AWS S3) to store raw and structured data.
Data Warehouse: Employed a cloud data warehouse (Snowflake, Amazon Redshift) for structured data storage, optimized for querying and analytics.
C. Data Transformation and ETL:
Apache Spark: Used for large-scale data processing and transformation tasks.
Airflow: Orchestrated ETL pipelines, scheduling, and monitoring.
D. Analytics and Visualization Layer:
Microsoft Power BI: Chose visualization tools for creating interactive dashboards and reports.
Jupyter Notebooks: Supported data exploration and advanced analytics using Python and machine learning libraries.
E. Data Governance and Security: Implemented access controls, encryption, and compliance measures to ensure data security and privacy. E.g., Row level Security, Access to specific Reports in Power BI etc
Self-Service Analytics: As Power BI is user friendly for creating reports and dashboards, we gave access to datasets so that Citizen developers can create their own reports. Also, we have defined data dictionary so that Business users can use measures and do not have to worry about DAX like features. We made heavy use of Power BI QnA visual for generating more visuals and reports.
Results
28%↑
Increase in Traffic to Conversion ratio
The platform provided instant insights into Customer Behavior, allowing the company to Optimize Marketing Campaigns and Website Performance in real time.
5x
Increased Data Volumes
The platform seamlessly handled increased data volumes by 5x as the business expanded without performance degradation.
50%↓
Decrease in Service Requests
Business users were able to independently explore data and create their own visualizations, reducing dependency on IT
25%↓
Reduction in Inventory Cost
Machine learning models were developed to predict customer preferences and optimize inventory levels.
By building this Modern Data Platform, the e-commerce company positioned itself as a Data-Driven Organization, gaining a competitive edge in the market and experiencing significant growth in revenue and customer satisfaction.
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